用PyTorch复现PointNet++

以下是使用PyTorch复现PointNet的基本步骤：

1. 导入必要的库

import torch
import torch.nn as nn
import torch.nn.functional as F

2. 定义PointNet的基本操作

class TNet(nn.Module):
    def __init__(self, k=3):
        super(TNet, self).__init__()
        self.conv1 = nn.Conv1d(k, 64, 1)
        self.conv2 = nn.Conv1d(64, 128, 1)
        self.conv3 = nn.Conv1d(128, 1024, 1)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, k*k)

    def forward(self, x):
        batch_size = x.size(0)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = torch.max(x, 2, keepdim=True)[0]
        x = x.view(-1, 1024)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        iden = torch.eye(x.size(1)).view(1, x.size(1)*x.size(1)).repeat(batch_size,1)
        if x.is_cuda:
            iden = iden.cuda()
        x = x + iden
        x = x.view(-1, x.size(1), x.size(1))
        return x

3. 定义PointNet的分类模型

class PointNetCls(nn.Module):
    def __init__(self, k=2):
        super(PointNetCls, self).__init__()
        self.tnet1 = TNet(k=3)
        self.conv1 = nn.Conv1d(3, 64, 1)
        self.conv2 = nn.Conv1d(64, 64, 1)
        self.tnet2 = TNet(k=64)
        self.conv3 = nn.Conv1d(64, 128, 1)
        self.conv4 = nn.Conv1d(128, 1024, 1)
        self.fc1 = nn.Linear(1024, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, k)

    def forward(self, x):
        batch_size = x.size(0)
        x = self.tnet1(x)
        x = torch.bmm(x, x.transpose(2,1))
        x = x.unsqueeze(1)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = torch.max(x, 2, keepdim=True)[0]
        x = x.repeat(1, 1, 1024)
        x = torch.cat([x, self.tnet2(x)], 1)
        x = F.relu(self.conv3(x))
        x = self.conv4(x)
        x = torch.max(x, 2, keepdim=True)[0]
        x = x.view(-1, 1024)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return F.log_softmax(x, dim=1)

4. 定义损失函数和优化器

model = PointNetCls()
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

5. 进行训练

for epoch in range(50):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' %(epoch+1, running_loss/len(trainloader)))

6. 进行测试

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        inputs, labels = data
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
print('Accuracy: %.2f %%' % (100 * correct / total))

这就是使用PyTorch复现PointNet的基本步骤。可以根据需要进行调整和改进，以获得更好的性能。